Global SST Trends

Global Trends in Sea Surface Temperature

The 2020 global sea surface temperature anomalies have been loaded and displayed below to visualize how different areas of the ocean experience swings in temperature.

# Access information to netcdf on box
nc_year <- "2021"
anom_path <- str_c(oisst_path, "annual_anomalies/1982to2011_climatology/daily_anoms_", nc_year, ".nc")


# Load 2020 as stack
anoms_2020 <- stack(anom_path)

Last Month’s Mean

The following code will subset the anomalies for the last full month and plot the average sea surface temperature anomalies for that month:

# Get the mean temperature anomalies for the last full month
which_month <- str_sub(Sys.Date(), 6, 7)
which_month <- str_pad(as.numeric(which_month) - 1, width = 2, side = "left", pad = "0")

# Pull last month from the anomaly stack
last_month_dates <- which(str_sub(names(anoms_2020), 7, 8) == which_month)
last_month_avg   <- mean(anoms_2020[[last_month_dates]])

# Convert wgs84 raster to stars array
last_month_st <- st_as_stars(rotate(last_month_avg))


# Set palette limits to center it on 0 with scale_fill_distiller
limit <- max(abs(last_month_st[[1]]), na.rm = T) * c(-1,1)

# Plot global map
ggplot() +
  geom_stars(data = last_month_st) +
  geom_sf(data = world, fill = "gray90") +
  scale_fill_distiller(palette = "RdYlBu", na.value = "transparent", limit = limit) +
  map_theme +
  coord_sf(expand = FALSE) +
  guides("fill" = guide_colorbar(title = "Mean Sea Surface Temperature Anomaly",
                                 title.position = "top", 
                                 title.hjust = 0.5,
                                 barwidth = unit(4, "in"), 
                                 frame.colour = "black", 
                                 ticks.colour = "black"))

Warming Rates

Global warming rates are calculated by getting the average temperature for each year across all cells in the data, then using a linear regression for each to determine the annual warming rate in degrees Celsius.

The warming rates are then ranked from high to low to identify areas with the most rapid ocean warming. The following maps display the warming rates and the percentile ranks for the top 20% of warming rates, or rates above the 80th percentile of the data.

# Access information to warming rate netcdf files on box
rates_path <- paste0(oisst_path, "warming_rates/annual_warming_rates")

# Percentile cutoff for displaying warming rates on map
percentile_cutoff <- 80

2006-2020

####  Data Prep  ####

# 2006-2020
rates_stack_all <- stack(str_c(rates_path, "2006to2020.nc"), 
                         varname = "annual_warming_rate")
ranks_stack_all <- stack(str_c(rates_path, "2006to2020.nc"), 
                         varname = "rate_percentile")

rates_prepped <- reclassify_to_discrete(rates_stack = rates_stack_all, 
                                        ranks_stack = ranks_stack_all)

# separaate rates from rank order
rates_st <- rates_prepped$rates
ranks_st <- rates_prepped$ranks

Global Rates

####  Plot Setup  ####

# guide label
guide_lab <- expression("Annual Warming Rate - "~degree~C)

# ggplot using stars
ggplot() +
  geom_stars(data = rates_st) +
  geom_sf(data = world, fill = "gray90") + 
  scale_fill_viridis_c(na.value = "black", option = "plasma") +
  guides("fill" = guide_colorbar(title = guide_lab,
                                 title.position = "top", 
                                 title.hjust = 0.5,
                                 barwidth = unit(4, "in"), 
                                 frame.colour = "black", 
                                 ticks.colour = "black")) +
  map_theme +
  coord_sf(expand = FALSE) +
  labs(caption = str_c("Top ", percentile_cutoff, "% of Warming Rates Shown.",
                       "\n Data from Years: 2006-2020"))

Global Ranks

# ggplot using stars
ggplot() +
  geom_stars(data = ranks_st) +
  geom_sf(data = world, fill = "gray90") + 
  scale_fill_viridis_c(na.value = "black", option = "plasma") +
  guides("fill" = guide_colorbar(title = "Percentile of Annual Warming Rate",
                                 title.position = "top", 
                                 title.hjust = 0.5,
                                 barwidth = unit(4, "in"), 
                                 frame.colour = "black", 
                                 ticks.colour = "black")) +
  map_theme +
  coord_sf(expand = FALSE) +
  labs(caption = str_c("Top ", percentile_cutoff, "% of Warming Rates Shown.",
                       "\n Data from Years: 2006-2020"))

1982-2020

####  Data Prep  ####


# 1982-2020
rates_stack_all <- stack(str_c(rates_path, "1982to2020.nc"), 
                         varname = "annual_warming_rate")
ranks_stack_all <- stack(str_c(rates_path, "1982to2020.nc"), 
                         varname = "rate_percentile")

rates_prepped <- reclassify_to_discrete(rates_stack = rates_stack_all, 
                                        ranks_stack = ranks_stack_all,
                                        percentile_cutoff = percentile_cutoff)

rates_st <- rates_prepped$rates
rates_raw_st <- rates_prepped$rates_raw
ranks_st <- rates_prepped$ranks

Global Rates

####  Plot Setup  ####

# guide label
guide_lab <- expression("Annual Warming Rate - "~degree~C)

# ggplot using stars
ggplot() +
  geom_stars(data = rates_st) +
  #geom_stars(data = rates_raw_st) +
  geom_sf(data = world, fill = "gray90") + 
  scale_fill_viridis_c(na.value = "black", option = "plasma") +
  guides("fill" = guide_colorbar(title = guide_lab,
                                 title.position = "top", 
                                 title.hjust = 0.5,
                                 barwidth = unit(4, "in"), 
                                 frame.colour = "black", 
                                 ticks.colour = "black")) +
  map_theme +
  coord_sf(expand = FALSE) +
  labs(caption = str_c("Top ", percentile_cutoff, "% of Warming Rates Shown.",
                       "\n Data from Years: 1982-2020"))

Global Ranks

# ggplot using stars
ggplot() +
  geom_stars(data = ranks_st) +
  geom_sf(data = world, fill = "gray90") + 
  scale_fill_viridis_c(na.value = "black", option = "plasma") +
  guides("fill" = guide_colorbar(title = "Percentile of Annual Warming Rate",
                                 title.position = "top", 
                                 title.hjust = 0.5,
                                 barwidth = unit(4, "in"), 
                                 frame.colour = "black", 
                                 ticks.colour = "black")) +
  map_theme +
  coord_sf(expand = FALSE) +
  labs(caption = str_c("Top ", percentile_cutoff, "% of Warming Rates Shown.",
                       "\n Data from Years: 1982-2020"))

Shifting Baselines

In 2021 NOAA is transitioning standard climatologies from the 30-year period of 1982-2011 to a new period spanning 1992-2020.

# Load the climatology difference
clim_diff <- stack(str_c(oisst_path, "daily_climatologies/clim_shift_82to91baselines.nc"))


# Convert wgs84 raster to stars array
clim_shift_st <- st_as_stars(rotate(clim_diff))


# Set palette limits to center it on 0 with scale_fill_distiller
limit <- max(abs(clim_shift_st[[1]]), na.rm = T) * c(-1,1)

# Plot global map
ggplot() +
  geom_stars(data = clim_shift_st) +
  geom_sf(data = world, fill = "gray90") +
  scale_fill_distiller(palette = "RdYlBu", na.value = "transparent", limit = limit) +
  labs(subtitle = "Shift in Mean Annual Climate")

 

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A work by Adam A. Kemberling

Akemberling@gmri.org